License-Plate-Detection

License Plate Detection Using Deep Learning and the YOLO Algorithm.

NOTE: Click on the images to view them full-size.

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(Train YOLO v5 on a Custom Dataset)

Installation

# Clone the License-Plate-Detection Repository
git clone https://github.com/aligh993/License-Plate-Detection

# Navigate to the Cloned Directory
cd License-Plate-Detection

# Install Required Packages
pip install -r requirements.txt

NOTE: For Quick Start you can use Main.ipynb.

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Dataset

https://B2n.ir/yn5201

Place the labeled dataset, license_plate_dataset, you downloaded from Google Drive into ./License-Plate-Detection/Dataset/.

dataset.yaml file, which contains the address and information for the dataset (such as train/test/validation image addresses, class names, and number of classes), is located in the main directory of the project.

Train

Train the model on the License Plate Dataset. Pre-trained models download automatically from the latest YOLOv5 release. Alternatively, you can manually download the YOLOv5 Pre-trained Model yolov5l.pt from Ultralytics YOLOv5 and place it in ./pre_trained_model/.

# Fine-tuning on a Pre-trained model of yolov5
python ./train.py --img 640 --batch 16 --epochs 60 --data ./dataset.yaml --weights ./pre_trained_model/yolov5l.pt --cache

Test

Test the model on the dataset. Place the weight best.pt you downloaded from Google Drive into ./runs/train/yolov5-l/weights/.

# After training, this will provide the trained weights that you can use for testing.
python ./detect.py --source ./test_images/ --weight ./runs/train/yolov5-l/weights/best.pt

Validate

Validate the model on the dataset. Place the weight best.pt you downloaded from Google Drive into ./runs/train/yolov5-l/weights/.

# After training, this will provide the trained weights that you can use for validating.
python ./val.py --data ./dataset.yaml --weights ./runs/train/yolov5-l/weights/best.pt

wandb

To view mAP, loss, confusion matrix, and other metrics, sign in at www.wandb.ai.

pip install wandb

Contributing

1. Fork it (https://github.com/aligh993/License-Plate-Detection)
2. Create your feature branch (git checkout -b feature/fooBar)
3. Commit your changes (git commit -am 'Add some fooBar')
4. Push to the branch (git push origin feature/fooBar)
5. Create a new Pull Request